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- Sergey LevineSuggested from co-authorship
- Yoshua BengioSuggested from co-authorship
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Works125 from public data
- Toward Causal Representation Learning1,684
Fundamental concepts of causal inference are reviewed and related to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research.
- 829
- Inductive biases for deep learning of higher-level cognition491
This work considers a larger list of inductive biases that humans and animals exploit, focusing on those which concern mostly higher-level and sequential conscious processing, to help build AI systems benefiting from humans’ abilities in terms of flexible out-of-distribution and systematic generalization.
- Aligning Text-to-Image Diffusion Models with Reward Backpropagation322
AlignProp is proposed, a method that aligns diffusion models to downstream reward functions using end-to-end backpropagation of the reward gradient through the denoising process, and achieves higher rewards in fewer training steps than alternatives, while being conceptually simpler, making it a straightforward choice for optimizing diffusion models for differentiable reward functions of interest.
- 320
- Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning265
This work leverages Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals, to enhance consistency in intermediate steps.
- 215
- InfoBot: Transfer and Exploration via the Information Bottleneck191
This work proposes to learn about decision states from prior experience by training a goal-conditioned policy with an information bottleneck, and finds that this simple mechanism effectively identifies decision states, even in partially observed settings.
- 167
- SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement160
SWE-Search is proposed, a multi-agent framework that integrates Monte Carlo Tree Search (MCTS) with a self-improvement mechanism to enhance software agents' performance on repository-level software tasks and reveals how performance scales with increased inference-time compute through deeper search, providing a pathway to improve software agents without requiring larger models or additional training data.
- A Theory for Emergence of Complex Skills in Language Models128
A statistical framework that relates cross-entropy loss of LLMs to competence on the basic skills that underlie language tasks and Mathematical analysis showing that the Scaling Laws imply a strong form of inductive bias that allows the pre-trained model to learn very efficiently.
- 116
- Learning to Induce Causal Structure86
This work designs a neural network architecture that learns the mapping from both observational and interventional data to graph structures via supervised training on synthetic graphs and achieves state-of-the-art performance on naturalistic graphs for low sample complexity.
- Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models79
This work develops a methodology for designing and administering a new evaluation to measure ability to combine skills using GPT-4 as well as the open LLaMA-2 70B model, and sketches how the methodology can lead to a Skill-Mix based eco-system of open evaluations for AI capabilities of future models.
- 70
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- AI-Assisted Generation of Difficult Math Questions48
A design framework that combines the strengths of LLMs with a human-in-the-loop approach to generate a diverse array of challenging math questions and a striking relationship observed between models' performance on the new dataset, suggesting that successfully solving the question in MATH$^2$ requires a nontrivial combination of two distinct math skills.
- 47
- Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling46
This work studies a new algorithm called probe sampling, a mechanism that dynamically determines how similar a smaller draft model's predictions are to the target model's predictions for prompt candidates, which is able to accelerate other prompt optimization techniques and adversarial methods.
- DIBS: Diversity Inducing Information Bottleneck in Model Ensembles45
This paper explicitly optimize a diversity inducing adversarial loss for learning the stochastic latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data.
- 41
- Reasoning Robustness of LLMs to Adversarial Typographical Errors40
An Adversarial Typo Attack algorithm is designed that iteratively samples typos for words that are important to the query and selects the edit that is most likely to succeed in attacking, which shows that LLMs are sensitive to minimal adversarial typographical changes.
- 40
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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